On the other hand, data-driven approaches that use sampled/observed input-output data to abstract or over-approximate unknown dynamics using a bounded-error setting, where set-valued uncertainties are considered, have gained increased popularity over the last few years. The general objective of such data-driven methods is to find a set of known systems that share the most properties of interest with the unknown system dynamics [1]}, [2]}. Under the assumption that the unknown dynamics is Lipschitz continuous, [3]} provides a recursive algorithm to approximate upper and lower bounding functions for univariate functions, while [4]} introduced a novel computational approach for multivariate functions. The research in [5]} further extended this approach to unknown dynamics that are Hölder continuous. Nonetheless, to our knowledge, these approaches do not explicitly deal with noise and their effect on the abstraction.
